Portfolio selection based on graphs: Does it align with Markowitz-optimal portfolios?

Hüttner Amelie (), Mai Jan-Frederik () and Mineo Stefano ()
Additional contact information
Hüttner Amelie: Technical University of Munich, Chair of Mathematical Finance, Parkring 11, D- 85748 Garching, Germany
Mai Jan-Frederik: XAIA Investment GmbH, Sonnenstraße 19, D-80331 München, Germany
Mineo Stefano: XAIA Investment GmbH, Sonnenstraße 19, D-80331 München, Germany

Dependence Modeling, 2016, vol. 6, issue 1, 63-87

Abstract: Some empirical studies suggest that the computation of certain graph structures from a (large) historical correlation matrix can be helpful in portfolio selection. In particular, a repeated finding is that information about the portfolio weights in the minimum variance portfolio (MVP) from classical Markowitz theory can be inferred from measurements of centrality in such graph structures. The present article compares the two concepts from a purely algebraic perspective. It is demonstrated that this heuristic relationship between graph centrality and the MVP does not originate from a structural similarity between the two portfolio selection mechanisms, but instead is due to specific features of observed correlation matrices. This means that empirically found relations between both concepts depend critically on the underlying historical data. Repeated empirical evidence for a strong relationship is hence shown to constitute a stylized fact of financial return time series.

Keywords: Portfolio selection; correlation matrix; minimum spanning tree; network centrality; Markowitz (search for similar items in EconPapers)
Date: 2016
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/demo-2018-0004 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:vrs:demode:v:6:y:2016:i:1:p:63-87:n:4

DOI: 10.1515/demo-2018-0004

Access Statistics for this article

Dependence Modeling is currently edited by Giovanni Puccetti

More articles in Dependence Modeling from De Gruyter
Bibliographic data for series maintained by Peter Golla ().